Online mining of weighted fuzzy association rules

Abstract

Mining useful information and helpful knowledge from data transactions is evolving as an important research area. Current online techniques for mining association rules identify the relationship among transactions using binary values. However, transactions with quantitative values are commonly encountered in real-life applications. In this paper, we address this problem by introducing a fuzzy adjacency lattice, and then integrate the lattice structure with linguistic weights in a way to reflect the importance of items. Experiments conducted using synthetic data show the effectiveness of the proposed method for online generation of weighted fuzzy association rules.